Overview

Dataset statistics

Number of variables22
Number of observations39607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory176.0 B

Variable types

Numeric21
Categorical1

Alerts

X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_13 is highly correlated with X_14High correlation
X_14 is highly correlated with X_13 and 2 other fieldsHigh correlation
X_15 is highly correlated with X_14High correlation
X_16 is highly correlated with X_14High correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 4 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_06 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_07 is highly correlated with Y_04 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_05 and 7 other fieldsHigh correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_13 is highly correlated with X_14High correlation
X_14 is highly correlated with X_13 and 2 other fieldsHigh correlation
X_15 is highly correlated with X_14High correlation
X_16 is highly correlated with X_14High correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 8 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_10High correlation
Y_07 is highly correlated with Y_04 and 2 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 7 other fieldsHigh correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_14 is highly correlated with X_15High correlation
X_15 is highly correlated with X_14High correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05High correlation
Y_05 is highly correlated with Y_04High correlation
Y_06 is highly correlated with Y_08 and 6 other fieldsHigh correlation
Y_08 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_09 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_10 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_11 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_12 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_13 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_14 is highly correlated with Y_06 and 6 other fieldsHigh correlation
X_10 is highly correlated with X_11High correlation
X_11 is highly correlated with X_10High correlation
X_13 is highly correlated with X_14 and 2 other fieldsHigh correlation
X_14 is highly correlated with X_13 and 2 other fieldsHigh correlation
X_15 is highly correlated with X_13 and 2 other fieldsHigh correlation
X_16 is highly correlated with X_13 and 2 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 4 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_01 and 8 other fieldsHigh correlation
Y_07 is highly correlated with Y_01 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_10 is highly correlated with Y_01 and 10 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 8 other fieldsHigh correlation
X_10 is highly skewed (γ1 = 35.30464523) Skewed
X_10 has 39575 (99.9%) zeros Zeros

Reproduction

Analysis started2022-08-06 10:01:09.979663
Analysis finished2022-08-06 10:02:15.650431
Duration1 minute and 5.67 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

X_09
Real number (ℝ≥0)

Distinct11137
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.3974696
Minimum37.58
Maximum637.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:15.745178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37.58
5-th percentile109.826
Q1188.54
median234.45
Q3263.96
95-th percentile300.4
Maximum637.49
Range599.91
Interquartile range (IQR)75.42

Descriptive statistics

Standard deviation66.7347248
Coefficient of variation (CV)0.2960757497
Kurtosis5.596274983
Mean225.3974696
Median Absolute Deviation (MAD)36.77
Skewness0.5403624022
Sum8927317.58
Variance4453.523494
MonotonicityNot monotonic
2022-08-06T19:02:15.889415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.58860
 
2.2%
254.7817
 
< 0.1%
259.1617
 
< 0.1%
255.4716
 
< 0.1%
253.3416
 
< 0.1%
251.3316
 
< 0.1%
256.3616
 
< 0.1%
263.4115
 
< 0.1%
252.7315
 
< 0.1%
268.0615
 
< 0.1%
Other values (11127)38604
97.5%
ValueCountFrequency (%)
37.58860
2.2%
87.611
 
< 0.1%
87.761
 
< 0.1%
87.812
 
< 0.1%
87.911
 
< 0.1%
87.931
 
< 0.1%
87.941
 
< 0.1%
87.982
 
< 0.1%
88.061
 
< 0.1%
88.071
 
< 0.1%
ValueCountFrequency (%)
637.491
< 0.1%
635.751
< 0.1%
635.61
< 0.1%
635.271
< 0.1%
635.121
< 0.1%
634.731
< 0.1%
634.371
< 0.1%
633.961
< 0.1%
633.891
< 0.1%
633.681
< 0.1%

X_10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002449062034
Minimum0
Maximum3.6
Zeros39575
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:16.012367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.6
Range3.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08625452732
Coefficient of variation (CV)35.21941303
Kurtosis1248.79761
Mean0.002449062034
Median Absolute Deviation (MAD)0
Skewness35.30464523
Sum97
Variance0.007439843483
MonotonicityNot monotonic
2022-08-06T19:02:16.112851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
039575
99.9%
2.912
 
< 0.1%
312
 
< 0.1%
3.34
 
< 0.1%
3.12
 
< 0.1%
3.61
 
< 0.1%
3.21
 
< 0.1%
ValueCountFrequency (%)
039575
99.9%
2.912
 
< 0.1%
312
 
< 0.1%
3.12
 
< 0.1%
3.21
 
< 0.1%
3.34
 
< 0.1%
3.61
 
< 0.1%
ValueCountFrequency (%)
3.61
 
< 0.1%
3.34
 
< 0.1%
3.21
 
< 0.1%
3.12
 
< 0.1%
312
 
< 0.1%
2.912
 
< 0.1%
039575
99.9%

X_11
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.6 KiB
0.0
39580 
0.5
 
12
0.6
 
11
0.4
 
3
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters118821
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039580
99.9%
0.512
 
< 0.1%
0.611
 
< 0.1%
0.43
 
< 0.1%
0.71
 
< 0.1%

Length

2022-08-06T19:02:16.237034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-06T19:02:16.365592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.039580
99.9%
0.512
 
< 0.1%
0.611
 
< 0.1%
0.43
 
< 0.1%
0.71
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
079187
66.6%
.39607
33.3%
512
 
< 0.1%
611
 
< 0.1%
43
 
< 0.1%
71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79214
66.7%
Other Punctuation39607
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
079187
> 99.9%
512
 
< 0.1%
611
 
< 0.1%
43
 
< 0.1%
71
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.39607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common118821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
079187
66.6%
.39607
33.3%
512
 
< 0.1%
611
 
< 0.1%
43
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII118821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
079187
66.6%
.39607
33.3%
512
 
< 0.1%
611
 
< 0.1%
43
 
< 0.1%
71
 
< 0.1%

X_12
Real number (ℝ≥0)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.373227965
Minimum4.27
Maximum4.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:16.480947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.27
5-th percentile4.34
Q14.36
median4.37
Q34.39
95-th percentile4.41
Maximum4.49
Range0.22
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.02180605032
Coefficient of variation (CV)0.00498625969
Kurtosis0.3016831267
Mean4.373227965
Median Absolute Deviation (MAD)0.01
Skewness0.07644483348
Sum173210.44
Variance0.0004755038304
MonotonicityNot monotonic
2022-08-06T19:02:16.594140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4.389034
22.8%
4.376543
16.5%
4.365268
13.3%
4.394667
11.8%
4.354536
11.5%
4.43067
 
7.7%
4.342163
 
5.5%
4.411591
 
4.0%
4.331136
 
2.9%
4.42727
 
1.8%
Other values (12)875
 
2.2%
ValueCountFrequency (%)
4.271
 
< 0.1%
4.282
 
< 0.1%
4.315
 
< 0.1%
4.31124
 
0.3%
4.32291
 
0.7%
4.331136
 
2.9%
4.342163
 
5.5%
4.354536
11.5%
4.365268
13.3%
4.376543
16.5%
ValueCountFrequency (%)
4.491
 
< 0.1%
4.481
 
< 0.1%
4.476
 
< 0.1%
4.4622
 
0.1%
4.4526
 
0.1%
4.44117
 
0.3%
4.43269
 
0.7%
4.42727
 
1.8%
4.411591
4.0%
4.43067
7.7%

X_13
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1433350165
Minimum0.05
Maximum0.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:16.709804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1
Q10.13
median0.14
Q30.16
95-th percentile0.19
Maximum0.28
Range0.23
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.02533359595
Coefficient of variation (CV)0.1767439427
Kurtosis0.4257732968
Mean0.1433350165
Median Absolute Deviation (MAD)0.02
Skewness0.2067623888
Sum5677.07
Variance0.0006417910838
MonotonicityNot monotonic
2022-08-06T19:02:16.830182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.148608
21.7%
0.155427
13.7%
0.134910
12.4%
0.164494
11.3%
0.123881
9.8%
0.173070
 
7.8%
0.112623
 
6.6%
0.182032
 
5.1%
0.11397
 
3.5%
0.191109
 
2.8%
Other values (14)2056
 
5.2%
ValueCountFrequency (%)
0.055
 
< 0.1%
0.0632
 
0.1%
0.0777
 
0.2%
0.08223
 
0.6%
0.09628
 
1.6%
0.11397
 
3.5%
0.112623
 
6.6%
0.123881
9.8%
0.134910
12.4%
0.148608
21.7%
ValueCountFrequency (%)
0.281
 
< 0.1%
0.273
 
< 0.1%
0.261
 
< 0.1%
0.2512
 
< 0.1%
0.2416
 
< 0.1%
0.2347
 
0.1%
0.22109
 
0.3%
0.21294
 
0.7%
0.2608
1.5%
0.191109
2.8%

X_14
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.37220138
Minimum13.15
Maximum13.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:16.956713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.15
5-th percentile13.32
Q113.35
median13.37
Q313.39
95-th percentile13.42
Maximum13.49
Range0.34
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.02986518962
Coefficient of variation (CV)0.002233378692
Kurtosis0.7425478249
Mean13.37220138
Median Absolute Deviation (MAD)0.02
Skewness-0.03445414414
Sum529632.78
Variance0.0008919295512
MonotonicityNot monotonic
2022-08-06T19:02:17.086522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13.387672
19.4%
13.375149
13.0%
13.364732
11.9%
13.353972
10.0%
13.393560
9.0%
13.42923
 
7.4%
13.342669
 
6.7%
13.411970
 
5.0%
13.331801
 
4.5%
13.421254
 
3.2%
Other values (21)3905
9.9%
ValueCountFrequency (%)
13.151
 
< 0.1%
13.162
 
< 0.1%
13.192
 
< 0.1%
13.211
 
< 0.1%
13.232
 
< 0.1%
13.241
 
< 0.1%
13.252
 
< 0.1%
13.268
 
< 0.1%
13.2743
0.1%
13.2875
0.2%
ValueCountFrequency (%)
13.495
 
< 0.1%
13.4811
 
< 0.1%
13.4730
 
0.1%
13.4699
 
0.2%
13.45276
 
0.7%
13.44559
 
1.4%
13.43762
 
1.9%
13.421254
3.2%
13.411970
5.0%
13.42923
7.4%

X_15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.3819138
Minimum13.23
Maximum13.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:17.223768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.23
5-th percentile13.33
Q113.36
median13.38
Q313.41
95-th percentile13.42
Maximum13.5
Range0.27
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.02946832036
Coefficient of variation (CV)0.002202100596
Kurtosis-0.06509156886
Mean13.3819138
Median Absolute Deviation (MAD)0.02
Skewness-0.3254807889
Sum530017.46
Variance0.0008683819049
MonotonicityNot monotonic
2022-08-06T19:02:17.358256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
13.416361
16.1%
13.384797
12.1%
13.374467
11.3%
13.44359
11.0%
13.394205
10.6%
13.363777
9.5%
13.353225
8.1%
13.422171
 
5.5%
13.341942
 
4.9%
13.331186
 
3.0%
Other values (18)3117
7.9%
ValueCountFrequency (%)
13.231
 
< 0.1%
13.241
 
< 0.1%
13.252
 
< 0.1%
13.2611
 
< 0.1%
13.2714
 
< 0.1%
13.2827
 
0.1%
13.2988
 
0.2%
13.3151
 
0.4%
13.31328
0.8%
13.32586
1.5%
ValueCountFrequency (%)
13.52
 
< 0.1%
13.492
 
< 0.1%
13.483
 
< 0.1%
13.4724
 
0.1%
13.4666
 
0.2%
13.45156
 
0.4%
13.44520
 
1.3%
13.431135
 
2.9%
13.422171
 
5.5%
13.416361
16.1%

X_16
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.46385967
Minimum13.26
Maximum13.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:17.493574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.26
5-th percentile13.4
Q113.44
median13.47
Q313.49
95-th percentile13.52
Maximum13.61
Range0.35
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.03674144319
Coefficient of variation (CV)0.002728893801
Kurtosis0.1697251972
Mean13.46385967
Median Absolute Deviation (MAD)0.02
Skewness-0.0855703323
Sum533263.09
Variance0.001349933648
MonotonicityNot monotonic
2022-08-06T19:02:17.632422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
13.476789
17.1%
13.463890
9.8%
13.453836
9.7%
13.483185
8.0%
13.493030
7.7%
13.442946
7.4%
13.432453
 
6.2%
13.52421
 
6.1%
13.421987
 
5.0%
13.511842
 
4.7%
Other values (24)7228
18.2%
ValueCountFrequency (%)
13.261
 
< 0.1%
13.273
 
< 0.1%
13.282
 
< 0.1%
13.32
 
< 0.1%
13.311
 
< 0.1%
13.335
 
< 0.1%
13.349
 
< 0.1%
13.3534
 
0.1%
13.36101
0.3%
13.37187
0.5%
ValueCountFrequency (%)
13.611
 
< 0.1%
13.63
 
< 0.1%
13.5910
 
< 0.1%
13.5821
 
0.1%
13.5764
 
0.2%
13.56117
 
0.3%
13.55299
 
0.8%
13.54459
 
1.2%
13.53951
2.4%
13.521400
3.5%

Y_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2249
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.353813796
Minimum0.017
Maximum4.409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:17.793039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.7833
Q11.1275
median1.349
Q31.576
95-th percentile1.931
Maximum4.409
Range4.392
Interquartile range (IQR)0.4485

Descriptive statistics

Standard deviation0.3562231101
Coefficient of variation (CV)0.2631256316
Kurtosis1.210970899
Mean1.353813796
Median Absolute Deviation (MAD)0.224
Skewness0.1502434869
Sum53620.503
Variance0.1268949041
MonotonicityNot monotonic
2022-08-06T19:02:17.944134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.37664
 
0.2%
1.31262
 
0.2%
1.33360
 
0.2%
1.4260
 
0.2%
1.38960
 
0.2%
1.27860
 
0.2%
1.360
 
0.2%
1.30859
 
0.1%
1.458
 
0.1%
1.26358
 
0.1%
Other values (2239)39006
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.0192
< 0.1%
0.023
< 0.1%
0.0212
< 0.1%
0.0252
< 0.1%
0.0262
< 0.1%
0.0271
 
< 0.1%
0.0281
 
< 0.1%
0.0351
 
< 0.1%
ValueCountFrequency (%)
4.4091
< 0.1%
4.0811
< 0.1%
3.791
< 0.1%
3.721
< 0.1%
3.5291
< 0.1%
3.5181
< 0.1%
3.5011
< 0.1%
3.4991
< 0.1%
3.4191
< 0.1%
3.3641
< 0.1%

Y_02
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2227
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057267251
Minimum0.007
Maximum3.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:18.102155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.45
Q10.793
median1.044
Q31.3
95-th percentile1.711
Maximum3.998
Range3.991
Interquartile range (IQR)0.507

Descriptive statistics

Standard deviation0.386265985
Coefficient of variation (CV)0.3653437527
Kurtosis0.6736418075
Mean1.057267251
Median Absolute Deviation (MAD)0.254
Skewness0.3657652688
Sum41875.184
Variance0.1492014112
MonotonicityNot monotonic
2022-08-06T19:02:18.258621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07259
 
0.1%
0.81459
 
0.1%
1.1258
 
0.1%
1.04357
 
0.1%
0.92456
 
0.1%
0.88856
 
0.1%
1.03155
 
0.1%
1.14455
 
0.1%
0.90255
 
0.1%
0.83454
 
0.1%
Other values (2217)39043
98.6%
ValueCountFrequency (%)
0.0072
 
< 0.1%
0.0084
< 0.1%
0.0093
< 0.1%
0.014
< 0.1%
0.0113
< 0.1%
0.0123
< 0.1%
0.0135
< 0.1%
0.0142
 
< 0.1%
0.0157
< 0.1%
0.0166
< 0.1%
ValueCountFrequency (%)
3.9981
< 0.1%
3.971
< 0.1%
3.721
< 0.1%
3.5521
< 0.1%
3.2881
< 0.1%
3.2561
< 0.1%
3.2281
< 0.1%
3.1421
< 0.1%
3.1151
< 0.1%
3.0491
< 0.1%

Y_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2127
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.014001717
Minimum0.017
Maximum3.756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:18.424268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.451
Q10.769
median0.998
Q31.239
95-th percentile1.628
Maximum3.756
Range3.739
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3614919509
Coefficient of variation (CV)0.3565003341
Kurtosis0.7764764849
Mean1.014001717
Median Absolute Deviation (MAD)0.235
Skewness0.396399124
Sum40161.566
Variance0.1306764305
MonotonicityNot monotonic
2022-08-06T19:02:18.577246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88866
 
0.2%
0.98863
 
0.2%
0.97362
 
0.2%
0.97161
 
0.2%
0.96561
 
0.2%
1.10460
 
0.2%
0.86960
 
0.2%
0.99959
 
0.1%
0.90657
 
0.1%
0.84657
 
0.1%
Other values (2117)39001
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.0214
< 0.1%
0.0221
 
< 0.1%
0.0242
< 0.1%
0.0252
< 0.1%
0.0272
< 0.1%
0.0293
< 0.1%
0.032
< 0.1%
0.0311
 
< 0.1%
ValueCountFrequency (%)
3.7561
< 0.1%
3.7131
< 0.1%
3.2841
< 0.1%
3.2131
< 0.1%
3.1981
< 0.1%
3.1821
< 0.1%
3.1021
< 0.1%
3.0991
< 0.1%
3.0691
< 0.1%
3.0281
< 0.1%

Y_04
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10773
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.62119133
Minimum-0.331
Maximum98.794
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size309.6 KiB
2022-08-06T19:02:18.742600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.331
5-th percentile8.9393
Q111.822
median13.837
Q315.626
95-th percentile17.5587
Maximum98.794
Range99.125
Interquartile range (IQR)3.804

Descriptive statistics

Standard deviation2.686631665
Coefficient of variation (CV)0.1972391107
Kurtosis25.18483477
Mean13.62119133
Median Absolute Deviation (MAD)1.887
Skewness0.4534505598
Sum539494.525
Variance7.217989702
MonotonicityNot monotonic
2022-08-06T19:02:18.895755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.85215
 
< 0.1%
14.24314
 
< 0.1%
13.34914
 
< 0.1%
15.70713
 
< 0.1%
15.46113
 
< 0.1%
13.8913
 
< 0.1%
14.40913
 
< 0.1%
14.77413
 
< 0.1%
15.06313
 
< 0.1%
15.43613
 
< 0.1%
Other values (10763)39473
99.7%
ValueCountFrequency (%)
-0.3311
< 0.1%
-0.3271
< 0.1%
-0.3081
< 0.1%
2.2421
< 0.1%
3.3121
< 0.1%
3.4471
< 0.1%
3.4781
< 0.1%
3.8331
< 0.1%
3.8471
< 0.1%
3.9241
< 0.1%
ValueCountFrequency (%)
98.7941
< 0.1%
33.3331
< 0.1%
25.9561
< 0.1%
21.4621
< 0.1%
21.4421
< 0.1%
20.891
< 0.1%
20.4761
< 0.1%
20.3211
< 0.1%
20.2091
< 0.1%
20.2041
< 0.1%

Y_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10241
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.29046706
Minimum18.589
Maximum37.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:19.049179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.589
5-th percentile26.512
Q129.768
median31.71
Q333.184
95-th percentile34.709
Maximum37.25
Range18.661
Interquartile range (IQR)3.416

Descriptive statistics

Standard deviation2.543221628
Coefficient of variation (CV)0.08127784168
Kurtosis0.489914823
Mean31.29046706
Median Absolute Deviation (MAD)1.658
Skewness-0.7720326285
Sum1239321.529
Variance6.467976249
MonotonicityNot monotonic
2022-08-06T19:02:19.200542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.69218
 
< 0.1%
33.46517
 
< 0.1%
32.49116
 
< 0.1%
31.94915
 
< 0.1%
33.21515
 
< 0.1%
31.71315
 
< 0.1%
32.65915
 
< 0.1%
32.59315
 
< 0.1%
32.715
 
< 0.1%
32.79615
 
< 0.1%
Other values (10231)39451
99.6%
ValueCountFrequency (%)
18.5891
< 0.1%
19.3951
< 0.1%
19.7041
< 0.1%
20.0621
< 0.1%
20.0672
< 0.1%
20.1231
< 0.1%
20.1891
< 0.1%
20.241
< 0.1%
20.4171
< 0.1%
20.4621
< 0.1%
ValueCountFrequency (%)
37.251
< 0.1%
37.2251
< 0.1%
37.1012
< 0.1%
36.9951
< 0.1%
36.9791
< 0.1%
36.9151
< 0.1%
36.8681
< 0.1%
36.8371
< 0.1%
36.8081
< 0.1%
36.8061
< 0.1%

Y_06
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4269
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.52938208
Minimum-19.963
Maximum18.998
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)0.2%
Memory size309.6 KiB
2022-08-06T19:02:19.364980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19.963
5-th percentile15.177
Q116.146
median16.694
Q317.164
95-th percentile17.758
Maximum18.998
Range38.961
Interquartile range (IQR)1.018

Descriptive statistics

Standard deviation1.89301384
Coefficient of variation (CV)0.1145241747
Kurtosis270.339787
Mean16.52938208
Median Absolute Deviation (MAD)0.501
Skewness-15.02970347
Sum654679.236
Variance3.583501399
MonotonicityNot monotonic
2022-08-06T19:02:19.543006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.78238
 
0.1%
16.87236
 
0.1%
17.09933
 
0.1%
16.85932
 
0.1%
17.13832
 
0.1%
16.96732
 
0.1%
16.7232
 
0.1%
16.84731
 
0.1%
16.7631
 
0.1%
16.47531
 
0.1%
Other values (4259)39279
99.2%
ValueCountFrequency (%)
-19.9631
< 0.1%
-19.6021
< 0.1%
-19.5171
< 0.1%
-19.4721
< 0.1%
-19.4431
< 0.1%
-19.3671
< 0.1%
-19.3511
< 0.1%
-19.2521
< 0.1%
-19.232
< 0.1%
-19.0991
< 0.1%
ValueCountFrequency (%)
18.9981
< 0.1%
18.9921
< 0.1%
18.8881
< 0.1%
18.8571
< 0.1%
18.8241
< 0.1%
18.7861
< 0.1%
18.7531
< 0.1%
18.7281
< 0.1%
18.6921
< 0.1%
18.6851
< 0.1%

Y_07
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2394
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.155054107
Minimum0.502
Maximum5.299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:19.706709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.502
5-th percentile2.53
Q12.863
median3.126
Q33.4335
95-th percentile3.864
Maximum5.299
Range4.797
Interquartile range (IQR)0.5705

Descriptive statistics

Standard deviation0.4189399013
Coefficient of variation (CV)0.1327837454
Kurtosis0.7671083874
Mean3.155054107
Median Absolute Deviation (MAD)0.283
Skewness0.08450194006
Sum124962.228
Variance0.1755106409
MonotonicityNot monotonic
2022-08-06T19:02:19.860520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.00557
 
0.1%
2.9456
 
0.1%
3.13653
 
0.1%
2.98852
 
0.1%
3.12852
 
0.1%
3.03351
 
0.1%
3.27251
 
0.1%
2.97651
 
0.1%
3.0351
 
0.1%
3.0850
 
0.1%
Other values (2384)39083
98.7%
ValueCountFrequency (%)
0.5021
< 0.1%
0.6851
< 0.1%
0.7231
< 0.1%
0.8181
< 0.1%
0.8791
< 0.1%
0.9111
< 0.1%
0.9211
< 0.1%
0.9331
< 0.1%
0.9451
< 0.1%
0.9531
< 0.1%
ValueCountFrequency (%)
5.2991
< 0.1%
5.1181
< 0.1%
4.9991
< 0.1%
4.9911
< 0.1%
4.9821
< 0.1%
4.9271
< 0.1%
4.9181
< 0.1%
4.911
< 0.1%
4.8681
< 0.1%
4.851
< 0.1%

Y_08
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3672
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.29483879
Minimum-29.652
Maximum-23.785
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:02:20.012802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.652
5-th percentile-27.4447
Q1-26.689
median-26.254
Q3-25.855
95-th percentile-25.283
Maximum-23.785
Range5.867
Interquartile range (IQR)0.834

Descriptive statistics

Standard deviation0.6605368289
Coefficient of variation (CV)-0.0251203985
Kurtosis0.7493218708
Mean-26.29483879
Median Absolute Deviation (MAD)0.415
Skewness-0.4373902743
Sum-1041459.68
Variance0.4363089024
MonotonicityNot monotonic
2022-08-06T19:02:20.159856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.31441
 
0.1%
-26.09140
 
0.1%
-25.83840
 
0.1%
-26.43139
 
0.1%
-26.43539
 
0.1%
-26.0939
 
0.1%
-26.08138
 
0.1%
-26.37238
 
0.1%
-26.04537
 
0.1%
-26.12236
 
0.1%
Other values (3662)39220
99.0%
ValueCountFrequency (%)
-29.6521
< 0.1%
-29.6421
< 0.1%
-29.6051
< 0.1%
-29.5781
< 0.1%
-29.4521
< 0.1%
-29.3521
< 0.1%
-29.331
< 0.1%
-29.3241
< 0.1%
-29.3092
< 0.1%
-29.3061
< 0.1%
ValueCountFrequency (%)
-23.7851
< 0.1%
-24.0131
< 0.1%
-24.1171
< 0.1%
-24.1421
< 0.1%
-24.1581
< 0.1%
-24.1621
< 0.1%
-24.181
< 0.1%
-24.191
< 0.1%
-24.2071
< 0.1%
-24.2111
< 0.1%

Y_09
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3649
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.30862254
Minimum-29.523
Maximum-23.96
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:02:20.315619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.523
5-th percentile-27.44
Q1-26.702
median-26.266
Q3-25.871
95-th percentile-25.311
Maximum-23.96
Range5.563
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6535798156
Coefficient of variation (CV)-0.02484279877
Kurtosis0.7264309573
Mean-26.30862254
Median Absolute Deviation (MAD)0.414
Skewness-0.4318247115
Sum-1042005.613
Variance0.4271665753
MonotonicityNot monotonic
2022-08-06T19:02:20.462299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.3143
 
0.1%
-26.22841
 
0.1%
-26.2838
 
0.1%
-26.10638
 
0.1%
-26.26537
 
0.1%
-26.12337
 
0.1%
-26.0437
 
0.1%
-26.37537
 
0.1%
-26.1137
 
0.1%
-26.33136
 
0.1%
Other values (3639)39226
99.0%
ValueCountFrequency (%)
-29.5231
< 0.1%
-29.4771
< 0.1%
-29.471
< 0.1%
-29.4271
< 0.1%
-29.3921
< 0.1%
-29.3761
< 0.1%
-29.3511
< 0.1%
-29.3391
< 0.1%
-29.3381
< 0.1%
-29.3311
< 0.1%
ValueCountFrequency (%)
-23.961
< 0.1%
-23.9851
< 0.1%
-24.0911
< 0.1%
-24.1041
< 0.1%
-24.1551
< 0.1%
-24.161
< 0.1%
-24.1891
< 0.1%
-24.2191
< 0.1%
-24.2421
< 0.1%
-24.2761
< 0.1%

Y_10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4458
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.40006244
Minimum-31.119
Maximum-20.052
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:02:20.624285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-31.119
5-th percentile-23.9517
Q1-22.871
median-22.275
Q3-21.791
95-th percentile-21.195
Maximum-20.052
Range11.067
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.920952195
Coefficient of variation (CV)-0.04111382268
Kurtosis10.34745855
Mean-22.40006244
Median Absolute Deviation (MAD)0.529
Skewness-1.837054602
Sum-887199.273
Variance0.8481529455
MonotonicityNot monotonic
2022-08-06T19:02:20.774410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.01433
 
0.1%
-21.99533
 
0.1%
-21.91932
 
0.1%
-22.09632
 
0.1%
-22.32732
 
0.1%
-22.17631
 
0.1%
-22.09231
 
0.1%
-22.32931
 
0.1%
-22.06531
 
0.1%
-21.78630
 
0.1%
Other values (4448)39291
99.2%
ValueCountFrequency (%)
-31.1191
< 0.1%
-30.9491
< 0.1%
-30.9261
< 0.1%
-30.7881
< 0.1%
-30.6191
< 0.1%
-30.5871
< 0.1%
-30.5841
< 0.1%
-30.5481
< 0.1%
-30.5371
< 0.1%
-30.5071
< 0.1%
ValueCountFrequency (%)
-20.0521
 
< 0.1%
-20.0931
 
< 0.1%
-20.131
 
< 0.1%
-20.1471
 
< 0.1%
-20.2241
 
< 0.1%
-20.2351
 
< 0.1%
-20.2721
 
< 0.1%
-20.2883
< 0.1%
-20.311
 
< 0.1%
-20.3311
 
< 0.1%

Y_11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4309
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.32506113
Minimum19.844
Maximum26.703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:02:20.928127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.844
5-th percentile22.815
Q123.836
median24.42
Q324.9115
95-th percentile25.514
Maximum26.703
Range6.859
Interquartile range (IQR)1.0755

Descriptive statistics

Standard deviation0.8301968024
Coefficient of variation (CV)0.03412927919
Kurtosis0.7579205164
Mean24.32506113
Median Absolute Deviation (MAD)0.532
Skewness-0.6749349242
Sum963442.696
Variance0.6892267307
MonotonicityNot monotonic
2022-08-06T19:02:21.079721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.73734
 
0.1%
24.77634
 
0.1%
24.49633
 
0.1%
24.5832
 
0.1%
24.54232
 
0.1%
24.40932
 
0.1%
24.64432
 
0.1%
24.50931
 
0.1%
24.74131
 
0.1%
24.58831
 
0.1%
Other values (4299)39285
99.2%
ValueCountFrequency (%)
19.8441
< 0.1%
20.0311
< 0.1%
20.0451
< 0.1%
20.1011
< 0.1%
20.1751
< 0.1%
20.1941
< 0.1%
20.1991
< 0.1%
20.2951
< 0.1%
20.2981
< 0.1%
20.3341
< 0.1%
ValueCountFrequency (%)
26.7031
< 0.1%
26.6591
< 0.1%
26.6571
< 0.1%
26.5921
< 0.1%
26.5791
< 0.1%
26.5671
< 0.1%
26.5511
< 0.1%
26.5451
< 0.1%
26.4831
< 0.1%
26.481
< 0.1%

Y_12
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3673
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23776173
Minimum-29.544
Maximum-23.722
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:02:21.250181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.544
5-th percentile-27.38
Q1-26.63
median-26.198
Q3-25.799
95-th percentile-25.238
Maximum-23.722
Range5.822
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6563285123
Coefficient of variation (CV)-0.02501465327
Kurtosis0.7459825
Mean-26.23776173
Median Absolute Deviation (MAD)0.413
Skewness-0.4446574078
Sum-1039199.029
Variance0.430767116
MonotonicityNot monotonic
2022-08-06T19:02:21.398564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.02649
 
0.1%
-26.1741
 
0.1%
-26.07640
 
0.1%
-26.35839
 
0.1%
-26.11839
 
0.1%
-26.4438
 
0.1%
-26.29238
 
0.1%
-25.99837
 
0.1%
-25.91937
 
0.1%
-26.35437
 
0.1%
Other values (3663)39212
99.0%
ValueCountFrequency (%)
-29.5441
< 0.1%
-29.4531
< 0.1%
-29.4411
< 0.1%
-29.3671
< 0.1%
-29.3461
< 0.1%
-29.3411
< 0.1%
-29.3351
< 0.1%
-29.311
< 0.1%
-29.2871
< 0.1%
-29.2831
< 0.1%
ValueCountFrequency (%)
-23.7221
< 0.1%
-23.9471
< 0.1%
-23.951
< 0.1%
-24.0671
< 0.1%
-24.1511
< 0.1%
-24.161
< 0.1%
-24.2211
< 0.1%
-24.2281
< 0.1%
-24.2311
< 0.1%
-24.241
< 0.1%

Y_13
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3665
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23386934
Minimum-29.448
Maximum-23.899
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:02:21.556051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.448
5-th percentile-27.366
Q1-26.624
median-26.193
Q3-25.794
95-th percentile-25.2393
Maximum-23.899
Range5.549
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.6550900257
Coefficient of variation (CV)-0.02497115531
Kurtosis0.7518019689
Mean-26.23386934
Median Absolute Deviation (MAD)0.413
Skewness-0.4398630698
Sum-1039044.863
Variance0.4291429417
MonotonicityNot monotonic
2022-08-06T19:02:21.704166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.09742
 
0.1%
-26.18741
 
0.1%
-26.33641
 
0.1%
-26.2840
 
0.1%
-26.15139
 
0.1%
-26.34638
 
0.1%
-26.03938
 
0.1%
-25.96837
 
0.1%
-26.21336
 
0.1%
-25.99536
 
0.1%
Other values (3655)39219
99.0%
ValueCountFrequency (%)
-29.4481
< 0.1%
-29.4431
< 0.1%
-29.3751
< 0.1%
-29.3681
< 0.1%
-29.3551
< 0.1%
-29.351
< 0.1%
-29.3011
< 0.1%
-29.2921
< 0.1%
-29.2361
< 0.1%
-29.2261
< 0.1%
ValueCountFrequency (%)
-23.8991
< 0.1%
-23.9361
< 0.1%
-23.9651
< 0.1%
-24.0211
< 0.1%
-24.1171
< 0.1%
-24.1231
< 0.1%
-24.1771
< 0.1%
-24.1941
< 0.1%
-24.2051
< 0.1%
-24.211
< 0.1%

Y_14
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3682
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.24586843
Minimum-29.62
Maximum-23.856
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:02:21.862661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.62
5-th percentile-27.3817
Q1-26.64
median-26.204
Q3-25.809
95-th percentile-25.245
Maximum-23.856
Range5.764
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6559887312
Coefficient of variation (CV)-0.02499398078
Kurtosis0.734812393
Mean-26.24586843
Median Absolute Deviation (MAD)0.413
Skewness-0.4307872388
Sum-1039520.111
Variance0.4303212155
MonotonicityNot monotonic
2022-08-06T19:02:22.011674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.30346
 
0.1%
-26.02541
 
0.1%
-26.14839
 
0.1%
-25.85839
 
0.1%
-26.0839
 
0.1%
-26.17438
 
0.1%
-26.10538
 
0.1%
-25.83538
 
0.1%
-26.42437
 
0.1%
-26.06537
 
0.1%
Other values (3672)39215
99.0%
ValueCountFrequency (%)
-29.621
< 0.1%
-29.5291
< 0.1%
-29.4931
< 0.1%
-29.4341
< 0.1%
-29.341
< 0.1%
-29.3351
< 0.1%
-29.3121
< 0.1%
-29.2921
< 0.1%
-29.2821
< 0.1%
-29.281
< 0.1%
ValueCountFrequency (%)
-23.8561
< 0.1%
-24.0521
< 0.1%
-24.1372
< 0.1%
-24.1391
< 0.1%
-24.1651
< 0.1%
-24.1761
< 0.1%
-24.1921
< 0.1%
-24.1931
< 0.1%
-24.2081
< 0.1%
-24.2111
< 0.1%

Interactions

2022-08-06T19:02:12.119150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:13.868923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.638544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.483322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:22.226389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.948886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.835491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.626387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:34.767821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.445162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.396873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.271134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:46.021364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.285902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:52.175728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.860616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.627409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.402208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:03.196987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.519785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.328535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:12.247376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:13.995694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.771816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.608109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:22.353348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:25.083610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.963949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.760998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:34.893711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.582428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.529984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.396615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:46.155479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.419752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:52.301168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.986639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.766649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.542407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:03.329727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.647588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.455094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:12.384274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:14.134321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.918143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.746369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:22.488353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:25.224613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:28.100034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.902692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:35.026328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.727498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.670649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.533873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:46.298558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.561485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:52.433830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:55.120311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.903444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.681035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:03.472240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.785395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.591218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:12.509887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:14.256993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:17.048492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.878397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:22.611621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:25.356016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:28.227890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:31.033376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:35.146952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.861091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.812500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.657349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:46.429839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.692857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:52.555743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:55.244997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:58.030710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.808397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:03.601601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.911843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.716923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:12.636064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:14.382657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:17.177460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:20.003260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:22.734287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:25.487564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:28.353102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:31.168042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:35.268590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.996667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.944307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.782231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:46.560735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.825256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:52.677783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:55.371468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:58.157714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.935923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:03.730446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:07.038836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.843786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:12.774939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:14.518864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:17.318668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:20.140510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-06T19:01:59.489426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:02.280702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:05.574950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:08.419247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.195772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:14.095284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:15.842850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:18.679118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:21.445963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.176919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.016525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:29.831608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:32.713906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:36.688186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:39.551769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:42.462330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:45.247772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:48.466594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:51.355535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.108318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:56.843904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:59.618361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:02.411330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:05.706162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:08.548259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.326693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:14.222643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:15.979415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:18.811779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:21.571570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.304823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.151837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:29.960581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:32.853857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:36.813737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:39.703941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:42.595295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:45.376127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:48.603382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:51.492140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.232690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:56.972097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:59.747338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:02.540666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:05.839476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:08.678813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.455421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:14.349965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.116065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:18.944407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:21.697934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.431547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.285565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.089996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:32.989511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:36.938104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:39.841015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:42.728075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:45.503757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:48.738754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:51.625840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.356384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.100385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:59.874734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:02.670280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:05.970896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:08.808147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.583685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:14.484410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.251044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.084442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:21.832610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.564626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.427212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.228379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:33.132492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.069712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:39.984294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:42.869304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:45.638287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:48.880216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:51.766461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.487736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.234920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.009334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:02.805842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.111499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:08.943389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.717736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:14.613945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.380058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.216524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:21.961024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.689972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.563969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.357909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:34.492511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.195453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.122461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.003516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:45.765432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.016248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:51.902079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.613108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.362420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.139421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:02.938723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.248482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.072280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.857092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:14.742268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:16.509505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:19.349427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:22.089497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:24.817539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:27.699265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:30.498192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:34.630171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:37.319642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:40.259745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:43.137662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:45.892694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:49.151469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:52.037922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:54.736963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:01:57.495161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:00.268650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:03.067947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:06.383879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:09.201184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:11.987327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-06T19:02:22.772590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-06T19:02:23.020231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-06T19:02:23.265718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-06T19:02:23.509633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-06T19:02:14.963385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-06T19:02:15.437012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X_09X_10X_11X_12X_13X_14X_15X_16Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
0245.710.00.04.340.1813.3413.3713.402.0561.4561.68010.50229.63216.0834.276-25.381-25.529-22.76923.792-25.470-25.409-25.304
1233.610.00.04.380.1813.3313.3313.461.4461.1841.26818.50733.17916.7363.229-26.619-26.523-22.57424.691-26.253-26.497-26.438
2272.200.00.04.360.1513.3613.3613.391.2510.6650.78214.08231.80117.0802.839-26.238-26.216-22.16924.649-26.285-26.215-26.370
3255.360.00.04.330.2113.3013.3313.391.4641.0791.05216.97534.50317.1433.144-25.426-25.079-21.76524.913-25.254-25.021-25.345
4241.460.00.04.350.1613.3513.3413.410.9830.6460.68915.04732.60217.5693.138-25.376-25.242-21.07225.299-25.072-25.195-24.974
5241.850.00.04.380.1413.3713.3713.461.1550.6780.58011.76029.66216.2013.343-26.466-26.527-22.62124.064-26.489-26.536-26.426
6237.510.00.04.370.1413.3613.3813.432.1401.4381.68914.13732.73915.5732.418-27.581-28.038-23.35523.051-27.650-27.709-27.599
7238.270.00.04.370.1613.3413.3513.451.7691.5351.53417.95434.68018.2303.147-24.917-24.832-20.68926.138-24.539-24.538-24.668
8232.230.00.04.390.1413.3913.3613.471.3260.9450.88313.95229.12916.6083.931-25.890-25.801-22.52124.353-25.738-25.825-25.764
9228.220.00.04.350.1413.3713.3813.422.0041.7871.54816.88534.20918.1202.646-25.520-25.408-21.15925.961-25.353-25.567-25.470

Last rows

X_09X_10X_11X_12X_13X_14X_15X_16Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
39597200.870.00.04.360.1313.3713.3713.431.4891.3691.30315.68734.08917.5863.107-25.927-25.836-21.61125.399-25.850-25.867-25.587
39598283.360.00.04.340.1313.3713.3613.441.2990.6121.03217.95732.87016.8043.140-26.569-26.304-23.10224.660-26.259-26.410-26.365
39599217.600.00.04.360.1113.4413.4213.520.9490.8910.76717.70630.87717.0902.547-26.652-26.807-22.18824.737-26.783-26.694-26.771
39600254.480.00.04.350.1713.3513.3813.460.9980.5630.9118.87928.95716.4413.387-26.545-26.572-22.70524.084-26.618-26.677-26.530
39601199.550.00.04.360.1313.3413.3513.381.5561.4181.32812.59832.67116.9492.996-26.106-26.281-22.35924.661-26.134-26.300-26.306
39602298.050.00.04.360.1513.3713.3813.511.3821.2151.26310.87429.19416.5823.410-26.486-26.581-22.77224.261-26.491-26.584-26.580
39603270.670.00.04.400.1313.3613.3613.471.4820.6061.0838.75929.85915.6593.406-27.308-27.203-24.67423.427-27.250-27.334-27.325
39604198.070.00.04.380.1413.3813.3913.471.1171.1540.99313.15924.72016.8233.215-26.502-26.687-22.57724.301-26.388-26.425-26.601
39605275.520.00.04.330.1613.3613.3813.450.8950.1870.4779.12326.41215.7574.216-26.760-26.634-24.06623.305-26.536-26.751-26.635
39606276.060.00.04.380.1413.3913.3813.481.1470.3480.85210.42130.74516.7813.307-26.054-26.251-23.25724.450-26.224-26.256-26.093